Face routing has been adopted in wireless sensor networks (WSNs) where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency.

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera-subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

"Deep fake" technologies are often associated with face-swapping algorithms based on images or videos. Although much research is focused on developing these techniques, there is still room for improvement, especially in human and visual assessment. In this study, we present an innovative approach to the problem of face replacement based on one model while preserving background features. Our model uses the Adaptive Attentional Denormalization method from FaceShifter while integrating identity features from the ArcFace and BiSeNet models to support the attribute extraction process. We used Fast GAN as a generative model to optimize and accelerate the training process on smaller data sets. Our research proves the effectiveness of the proposed approach by combining innovative methods and architectures in one coherent solution.

We employed the Riemannian Face Manifold (RFM) as an alternative approach to the conventional linear Euclidean space for explaining the atypicality bias in face likeliness judgments. The RFM posits that the mental representation of faces is better captured as a manifold of stable states, accounting for the nonlinearity in the physical properties of faces. To examine the relationship between physical and psychological distance of morph and typical/atypical faces, we manipulated the parameter k and incorporated a weight function in the Riemannian metric. Our results indicate that the psychological distance between the morph and typical face was longer than that between the morph and atypical face, consistent with prior research on the atypicality bias in perceptual similarity. The RFM approach provides mathematical support and is a powerful tool for studying face perception and recognition, offering potential implications for explaining the other-race effect. Future research can utilize the RFM to investigate individual differences in face recognition abilities and expertise, and how they impact psychological distance and face processing.

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the creativity of the project, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data was collected from multiple crowdfunding platforms for projects in Africa, that is, cross-country data from 54 African countries, to overcome data limitations from a single country. Econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges in the success of online crowdfunding. The signalling and agency theories support the finding. The findings were limited to crowdfunding projects on the African continent and are not necessarily applicable to some developed countries.

Fighting against the COVID-19 pandemic caused by the SARS-CoV-2 virus is one of the most critical challenges facing the global health system today. The possibility to identify the group of persons in the cohort of people under 50 years old, who are sensitive to the COVID-disease by non-invasive methods, is a very perspective approach for estimating the epidemiological state of the human population. The study aimed to identify the features of people's faces with COVID-19 that the most correlate with disease severity could serve as one of these approaches. For this aim, 525 photos of patients' faces with different outcomes of COVID-19 disease were analyzed using the Dlib face recognition convolutional neural network pre-trained for face recognition. Face descriptor vectors were obtained using the convolutional neural network. Facial features were found that predict a person's sensitivity to the SARS-CoV-2 virus (disease severity), and the contribution of each of the features to the risk of developing a severe form of COVID in a person was found. The accuracy of the binary classification of the individual severity of the COVID-19 course using the k-nearest neighbors algorithm on the test dataset was accuracy - 84%, AUC - 0.90.

The 21st century, where all things are depending upon technology, almost all the human tasks are being done with the help of technology to save a lot of time and make our life much more comfortable. The monitoring of students’ attendance is a crucial task for faculty in today’s world. There are more chances of errors while entering the students’ attendance records in the primary system, primarily when the class was over. The main concern of this study is to build an IoT-based automated attendance management system for educational institutes by biometric recognition to incorporate fake/proxy attendance and errors of entry and to replace old manual methods of taking students’ attendance by calling their names or roll numbers. The AAS will click the image of the classroom, and it will automatically detect the faces of students sitting in the lecture room and recognize them during lectures then mark their attendance daily to keep a record of their presence and also maintain and manage it for the management staff of the institution for future by using web services.

This work introduces a novel facial image capture system that utilizes computer vision technology and artificial intelligence for real-time detecting, tracking, and capturing of human faces. The objective of this study is to address the challenges posed by poor-quality facial images in biometric authentication, especially in passport photo acquisition and recognition. By combining face-aware capture technology with AES encryption for secure image storage, we present a completely open-source hardware solution that consists of a Jetson processor, a 16MP autofocus RGB camera, a custom enclosure, and a touch sensor LCD for user interaction. Pilot data collection demonstrates the system's ability to capture high-quality images, achieving a 98.98% accuracy in storing images of acceptable quality. The integration of AES encryption ensures data security, making the proposed system suitable for real-time applications in other domains beyond identity verification in passport applications, such as security systems, video conferencing, etc

The study is an attempt to enquire into the preference of undergraduate students, after a considerable and over a year-long experience with Online Teaching and Learning (OTL), under the emergency preventive measure of switching from the traditional face-to-face classes to online. The study followed an exploratory approach, with a quantitative survey followed by a qualitative one, and a convenient sampling method to collect responses from a substantial sample size. The study is positioned after more than a year of remote classes by the undergraduate student, and hence represents highly experienced reflections and preferences from these students, as compared to other studies conducted last year. The study has profound implications in considering, and questioning, the importance of on-campus classes, and significance of the physical presence of a tutor in the class, and its effect on the learning experience of undergraduate students.

The COVID-19 pandemic has led to the widespread use of face masks to reduce disease transmission, but one unintended consequence is that this may negatively impact sleep and arousal by altering the expression of yawning. Yawns are defined by an involuntary and powerful gaping of the jaw with inspiration, and the typical design of face masks may restrict and/or disrupt the mandibular jaw movement and counter-current heat exchange during this behavior. Face masks additionally impair the contagious nature of yawning by disrupting the recognition of yawning in others within social/community settings. Given that the neurophysiological consequences of yawning function in facilitating changes in state and arousal, and most notably transitions in waking and sleeping via thermoregulation, face mask usage could be an important variable to consider within sleep studies. We briefly outline these connections, highlight some important factors to consider, and discuss the implications for both prior and current research on sleep.

Nowadays, security is a top priority. In fact, biometrics uses cutting-edge technologies to identify terrorists and criminals. But the practice of distinguishing humans based on intrinsic physical or behavior traits goes back thousands of years. With the widespread use of computers in the late 20th century, new possibilities for digital biometrics emerged and new technologies were generously used. Among these, we remember high resolution security video cameras and drones. So, the aim of the present project is to study and explain the features of these technologies, especially the ones of the the Phantom 4 Pro+ aircraft and analyze its operating methods in order to identify human faces during live streaming of videos. For this purpose, it will be used Paul Viola and Michael Jones’ face detection algorithm, which includes Haar features and cascade classifiers to identify faces, eyes and ears of an individual.

Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex & Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.

A clinical simulation web-based program, known as FIRST2ACT (Feedback Incorporating Review and Simulation Techniques to Act on Clinical Trends) is designed to increase the efficacy of cli-nicians’ actions in the recognition and immediate response to a patient’s deterioration. This study, which was nested in a larger mixed method project, used ten focus groups (n=65) of graduate, enrolled, registered nurses, Associate Nurse Unit Managers, and general manag-ers/educators/coordinators from four different institutions to investigate whether nurses felt their practice was influenced by participating in either a face-to-face or web-based simulation educa-tional programme about patient deterioration. Results indicate individuals who were less ‘tech-savvy’ appreciated the flexibility of web-based learning, which increased their confidence. Face-to-face students appreciated self-reflection through performance evaluation. While face-to-face simulations were unable to completely duplicate symptoms, they did show nurses' adaptability. Both interventions enhanced clinical practice by improving documentation and re-plies while also boosting confidence and competence. Web learners initially experienced tech-related anxiety, which gradually subsided, demonstrating healthcare professionals' resilience to new learning approaches. Overall, the study highlighted the advantages and challenges of web-based and face-to-face education in clinical practice, emphasising the importance of adapta-bility and reflective learning for healthcare professionals. Further exploration of specific topics is required to improve practice, encourage knowledge sharing among colleagues, and improve early detection of patient deterioration.

During the COVID-19 outbreak, most university courses have been offered on online platforms. A sudden shift from face-to-face classroom learning to online formats could influence the learning effectiveness of classes. This study aims to investigate differences in the learning effectiveness of online and face-to-face lecture courses. It also explores factors that impact the effectiveness of online instruction. These factors include interactions among learners, interactions between learners and the instructor, the quality of online platforms, learners’ ability to use devices and follow instructions, and learners’ situational challenges. The study participants were 261 university students at King Mongkut’s University of Technology Thonburi in Bangkok, Thailand. All participants were enrolled in at least one lecture course, such as history, humans and the environment, the environment and development, or general philosophy, during the 2019 academic year. A questionnaire was distributed to participants after they completed these courses in May 2020. Paired simple t-test analyses were used to compare the effectiveness of online and face-to-face classes, and a multiple regression analysis was used to identify factors that impact the learning effectiveness of online classes. The results show that online classes are less effective than face-to-face courses. The multiple regression analysis also revealed that the effectiveness of online learning was significantly impacted by learners’ ability to interact with classmates during class, their ability to interact with instructors after the class, the quality of online platforms, and disturbances or distractions in learners’ environments.

Consuming fish sustainably means consuming less and better, considering environmental, social, and economic impacts. This demands more knowledge to make informed decisions. In this study, the main goal was to understand how sustainability education can be used as a tool to raise awareness and promote new habits regarding the sustainable consumption of fish in 4th-grade students at primary school. During the COVID-19 pandemic, face-to-face and online learning methodologies were used in two different groups of students to understand whether the teaching method impacted learning. A sequential explanatory mixed methodology was applied, with a pre-test/post-test design, combined with focus group sessions. Between the pre and post-tests three learning modules were performed. The results showed that the students were unaware of many aspects related to fish sustainable consumption, but the developed activities significantly increased their knowledge of the subject and impacted students to change their consumption behaviours. It was also found that the construction of knowledge by the students after the activities was achieved either by face-to-face or online learning. Interviews from focus group sessions showed that students got to know new fish species, learned about the concept of minimum landing size and closure season and that fish are labelled like other types of food.

Face recognition is a key computer vision task that focuses on identifying or verifying individuals using their facial features. This task becomes more difficult with low-resolution images, where the reduced pixel count and detail make it harder to extract and match features accurately. In this study, we assess the effectiveness of Multilinear Side-Information-based Discriminant Analysis (MSIDA) on low-resolution images, using the CelebA database as a reference. The system showed strong performance, achieving 90.60% accuracy on high-resolution and 88.23% on low-resolution images, highlighting the robustness and effectiveness of MSIDA.

One of the most crucial elements in deeply understanding humans on a psychological level is manifested through facial expressions. The analysis of a human behavior can be informed by their facial expressions, making it essential to employ indicators such as expression (Expr), valence-arousal (VA), and action units (AU). In this paper, we introduce the method proposed in the Challenge of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) at CVPR 2024. Our proposed method utilizes the multi-modal Aff-wild2 dataset, which is splitted into spatial and audio modalities. For the spatial data, we extract features using a SimMiM model that was pre-trained on a diverse set of facial expression data. For the audio data, we extract features using a WAV2VEC model. To fusion the extracted spatial and audio features, we employed the cascaded cross-attention mechanism of a transformer.

Background/Aim: Rapid onset of facial ptosis can impact physical appearance and compromise the outcomes of facelift procedures. The level of vitamin D has a potential correlation with collagen formation and its deficiency with inflammatory processes that affect the breakdown of hyaluronic acid. This study aims to investigate the potential relationship between accentuated facial ptosis in women and low levels of vitamin D. Furthermore, it aims to explore preventive measures or strategies to slow down facial ptosis and enhance the longevity of facelift results. Materials and Methods: The study was focused on monitoring the vitamin D levels in women and men with advanced facial ptosis and comparing them with a control group. Results: Notably, a direct association between gender and serum vitamin D levels was observed, indicating less sustainable outcomes in women. Conclusion: Women face additional challenges in the aging process due to hormonal shifts after menopause or premenopausal, which are associated with osteoporosis and lower vitamin D levels.

Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and different lighting and pose conditions. One way to detect faces is to utilize a highly advanced face detection method, such as RetinaFace, which uses deep learning techniques to achieve high accuracy in various datasets. However, even the best face detectors can produce false positives, which can lead to incorrect or unreliable results. In this paper, we propose a method for reducing false positives in face detection by using information from a depth map. A depth map is a two-dimensional representation of the distance of objects in an image from the camera. By using the depth information, the proposed method is able to better differentiate between true faces and false positives. The authors evaluate their method on a combined dataset of 549 images, containing a total of 614 upright frontal faces. The results show that the proposed method is able to significantly reduce the number of false positives without sacrificing the overall detection rate. This indicates that the use of depth information can be a useful tool for improving face detection performance.

Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach are better than those obtained by state of the art methods. On the REPLAY-ATTACK database, we were able to attain a Half Total Error Rate (HTER) of 0.62% and an Equal Error Rate (EER) of 0.58%. It was possible to attain an EER of 0% on both the CASIA-FASD and the MSU FAS databases.

In the current Sars-CoV-2 pandemic, wearing a face mask was mandatory and is still desired during school lessons. There are no controlled studies in children to date indicating an effect on cognitive performance wearing face masks. In a randomized controlled trial, we analysed the influence of face masks on cognitive performance of pupils during regular school lessons. Pupils (n=133, 5th to 7th grade) were randomized by alternating allocation into control (with masks, n=65) and intervention groups (without mask, n=68). After two school lessons with (control) and without (intervention) face masks in class all pupils performed digital tests for cognitive performance regarding attention and executive functions (Switch, CORSI block tapping, 2-back and flanker task). Overall, there were no significant differences in cognitive performance between both groups, masks vs. no masks. Wearing face masks has no significant influence on attention and executive functions of pupils and can still be recommended during school lessons.

This study explored the electrocortical correlates of conscious and nonconscious perceptions of emotionally laden faces in neurotypical adult women with varying levels of autistic-like traits (Autism Spectrum Quotient - AQ). Event-related potentials (ERPs) were recorded during the viewing of backward-masked images for happy, neutral, and sad faces presented either below (16 ms - subliminal) or above the level of visual conscious awareness (167 ms - supraliminal). Sad compared to happy faces elicited larger frontal-central N1, N2, and occipital P3 waves. We observed larger N1 amplitudes to sad faces than to happy and neutral faces in High-AQ (but not Low-AQ) scorers. Additionally, High-AQ scorers had a relatively larger P3 at the occipital region to sad faces. Regardless of the AQ score, subliminal perceived emotional faces elicited shorter N1, N2, and P3 latencies than supraliminal faces. Happy and sad faces had shorter N170 latency in the supraliminal than subliminal condition. High-AQ participants had a longer N1 latency over the occipital region than Low-AQ ones. In Low-AQ individuals (but not in High-AQ ones), emotional recognition with female faces produced a longer N170 latency than with male faces. N4 latency was shorter to female faces than male faces. These findings are discussed in view of their clinical implications and extension to autism.

The current Coronavirus Disease 19 (COVID-19) pandemic, caused by the SARS-CoV-2 virus, is unprecedented in recent history and threatens the lives and livelihoods of billions worldwide. The rapid spread and lack of modern parallels have left governments, health agencies, and the public racing to understand how to best mitigate and ultimately suppress the pandemic, but we are still working to understand the strengths and weaknesses of various interventions at our disposal. Few issues have been as contentious as the public use of face masks to control the pandemic’s spread. There is ongoing debate about their effectiveness, but an increasing body of evidence suggests that masks could be a useful in preventing the spread of coronavirus, leading several governments and health agencies to review and revise their policies. Here, we review the theory and evidence behind use of masks. The theory behind masks is that they prevent the spread of viral particles by infected persons, and inhalation of viral particles by uninfected persons. Even assuming masks are not 100% effective in preventing infection, they may reduce severity of infection by reducing viral dosing. Laboratory studies suggest masks may be effective in stopping both exhalation and inhalation of viral particles. However, real-world studies provided limited evidence for the use of masks in controlling influenza transmission and highlight potential problems associated with their misuse, such as poor compliance or improper use. Evidence for efficacy of face masks against the first SARS virus, SARS-CoV-1, implies that they may be effective against the current outbreak of SARS-Cov-2 virus. This is important as mathematical modeling suggests that even small reductions of in transmission rates can make a large difference over time, potentially slowing the pace of viral pandemics and limiting their spread. Perhaps the strongest argument for the use of masks is that countries with early adoption of masks have tended to see flatter pandemic curves, even without strict nationwide lockdowns. There is little evidence that respirators are more effective than surgical masks, but this may be due to misuse or poor compliance. Studies suggest some non-medical masks perform on par with medical masks. Improvised masks are less effective than medical masks, but may provide better protection than nothing at all. While many governments now encourage the use of improvised face coverings, more will need to be known about material, design, and who needs to wear masks, and when, to ensure effectiveness. Proper use of masks will also be important; if masks are used improperly or infrequently they may provide limited protection. It is important that public health policy makers consider the debate and the potential of masks as part of multi-faceted coronavirus control strategies.

Complex illumination condition is one of the most critical challenging problems for practical face recognition. In this paper, we propose a novel method to improve the illumination invariants for solving this challenge. Firstly, a new method based on the Lambert reflectance model is proposed to extract illumination invariant, which is less insensitive to complex illumination variations. Secondly, in order to repair the defects caused by process of illumination invariants extraction, Fast Mean Filter is utilized to smooth and remove noise. Lastly, for raising the richness of information in output image, a nonlinear normalization transformation is proposed. Compared with the state-of-the-arts, experimental results show that the proposed method can extract more robust illumination invariants. Apart from it, the richness of information in processed image is greater and superior performance in face recognition rate is superior.

In this work, we present a real-time heart rate estimation method from a video using face detection, color-based signal extraction, and signal processing tech- niques. The proposed approach computes the heart rate by analyzing the color fluctuations in the facial region of interest (ROI). This report outlines the math- ematical formulation and results of the implementation.

The present study investigated how two types of face masks affected seven acoustic parameters, commonly used in forensic speaker recognition, in several languages. Reading samples from an excerpt of “The Little Prince” were recorded by volunteers of both genders speaking in Lithuanian, Croatian, Romanian, Turkish, Ukrainian, Portuguese, Georgian, Hungarian, Spanish, Russian, Polish, and German, without a protection mask, wearing a surgical mask, and wearing an FFP2 mask. In addition, recordings from mobile and landline communications in some of the mentioned languages were also obtained. A total of 860 volunteers were recorded. The dataset is part of the Forensic Multilingual Voices Database (FMVD), developed under the “Competency, Education, Research, Testing, Accreditation, and Innovation in Forensic Science” (CERTAIN-FORS) project, funded by the European Union (EU) and coordinated by the European Network of Forensic Science Institutes (ENFSI). The results showed that face masks have an impact on the studied acoustic parameters and that the effects vary with mask type, sex, language, and recording channel.

The twelve cranial nerves play a crucial role in nervous system, orchestrating a myriad of functions vital for our everyday life. These nerves are each specialized for particular tasks. Cranial Nerve I, known as the olfactory nerve, is responsible for our sense of smell, allowing us to perceive and distinguish various scents. Cranial Nerve II, or the optic nerve, is dedicated to vision, transmitting visual information from the eyes to the brain. Eye movements are governed by Cranial Nerves III, IV, and VI, ensuring our ability to track objects and focus. Cranial Nerve V controls facial sensation and jaw movement, while Cranial Nerve VII, the facial nerve, facilitates facial expressions and taste perception. Cranial Nerve VIII, or the vestibulocochlear nerve, plays a critical role in hearing and balance. Cranial Nerve IX, the glossopharyngeal nerve, affects throat sensation and taste perception. Cranial Nerve X, the vagus nerve, is a far-reaching nerve, influencing numerous internal organs such as the heart, lungs, and digestive system. Cranial Nerve XI, the accessory nerve, is responsible for neck muscle control, contributing to head movement. Finally, Cranial Nerve XII, the hypoglossal nerve, manages tongue movement, essential for speaking, swallowing, and breathing. Understanding these cranial nerves is fundamental in comprehending the intricate workings of our nervous system and the functions that sustain our daily lives.

This research aims to thoroughly understand the delicate interplay between stakeholders' perspectives, biometric data protection policies, and the deployment of face recognition technologies in Indonesia's dynamic digital world. Adopting a qualitative methodology, the study conducts Focus Group Discussions (FGDs) with various stakeholders, including government agencies, technology developers, regulatory agencies, civil society groups, and data subjects as end-users. Face-to-face group discussions facilitate an in-depth exploration of participants' perspectives, yielding rich qualitative data that reveals nuanced insights into the delicate balance between technological innovation, ethical considerations, and socioeconomic repercussions associated with implementing face recognition technology. The research comprehensively explains the difficulties and opportunities associated with responsible technological progress and biometric data security. The findings from the FGDs influence the development of strategies that effectively balance technical advancement with individual rights, privacy, and social well-being within the growing digital landscape of Indonesia. The regulatory function of the government seeks to bridge the gap between public expectations and technological advancements. Collaboration between academic institutions, government agencies, the private sector, and data subjects as end-users emerges as a crucial element, fostering an all-encompassing strategy integrating research, law, technology deployment, and user empowerment. This holistic approach is necessary to ensure that biometric data and facial recognition technology are utilized responsibly, thereby laying the way for Indonesia's technologically sophisticated and ethically aware digital future.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

The Covid 19 pandemic threaten the life of individuals and there was a lack of information in treatment, handling of patients and disposal of waste. The psychological and behavioral impact on Humans due to outbreak of Covid is studied and based on that a person-centered care center is suggested. The research methodology used are surveys, interviews among stake holders and design details of person-centered care center. Based on the surveys and interviews conducted during pandemic, it is found that 94% of the respondents prefer to use private vehicles with at least one member to ac-company them. People prefer to use 3 ply facemasks, followed by cotton masks and N95. Existing literature discusses the physical effects on the individual, but this paper focuses more on pros and cons on human life during the pandemic and lock down period. Based on the study, care center with facilities to treat patients with different levels of infections and counselling center for the persons suffered from Covid and other infectious diseases to overcome their psycho-logical and behavior changes are recommended.

While the desire to uncover the neural correlates of consciousness has taken numerous directions, self-face recognition has been a constant in attempts to isolate aspects of self-awareness. The neuroimaging revolution of the 1990’s bought about systematic attempts to isolate the underlying neural basis self-face recognition. These studies, including some of the first fMRI (functional Magnetic Resonance Imaging) studies, revealed a right hemisphere bias for self-face recognition in a diverse set of regions including the insula, the Dorsal Frontal Lobe, the Temporal Parietal Junction and Medial Temporal Cortex. Confirmation of these data (which are correlational) was provided by TMS (Transcranial Magnetic Stimulation) and patients in which direct inhibition or ablation of right hemisphere regions leads to a disruption or absence of self-face recognition. These data are consistent with a number of theories including a right hemisphere dominance for self-awareness and/or a right hemisphere specialization for identifying significant social relationships including to oneself.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Since December 2019, the COVID-19 virus has rapidly spread worldwide, prompting the World Health Organization (WHO) to declare it a pandemic and advocate for the widespread use of face masks to mitigate transmission. In this review, we delve into the potential impact of prolonged face mask use on temporomandibular joint (TMJ) health, an area that has garnered limited attention amidst COVID-19 research. Research has revealed that improper mask fit, and constant readjustment can lead to TMJ abnormalities. Similarly, there is a demonstrated correlation between continuous mask usage and increased incidence of headaches, temporomandibular pain, and diminished quality of life. Many studies have highlighted discomfort in the preauricular area, headaches, TMJ noises, headache, jaw pain, and muscle fatigue, as well as dermatological disorders that have been attributed to prolonged mask wear and its impact on TMJ. Our study catalyzes future research endeavors, urging a deeper exploration of the implications of long-term mask wear, not only in the context of the COVID-19 pandemic but also among occupational groups regularly exposed to extended mask use. By unraveling the complexities of TMJ health in the face of evolving preventive measures, we aim to enhance our understanding and safeguard the well-being of mask-wearers worldwide.

We investigate new graph characteristics namely total (vertex, edge) face irregularity strength of gen- eralized plane grid graphs Gmn under k-labeling Phi of type (Alpha, Beta, Gamma). The minimum integer k for which a vertex-edge labelled graph has distinct face weights is called the total face irregularity strength of the graph and is denoted by tfs(Gmn). In this article, the graphs G = (V;E; F) under consideration are simple, finite, undirected and planar. We will estimate the exact tight lower bounds for the total face irregularity strength of some families of generalized plane grid graphs.

Background: Different drugs damage the frontal cortices, particularly the prefrontal areas involved in both emotional and cognitive functions, with a consequence of decoding emotion deficits for people with substance abuse. The present study aims to explore the cognitive impairments in drug abusers through facial, body and disgust emotion recognition, expanding the investigation of emotions, processing, measuring accuracy and response velocity. Method: We enrolled 13 addicted to cocaine and 12 alcohol patients attending treatment services in Italy, comparing them with 33 matched controls. Facial emotion and body posture recognition tasks, a disgust rating task, and the Barrat Impulsivity Scale were included in the experimental assessment. Results: We found that emotional processes are differently influenced by cocaine and alcohol, suggesting that these substances impact diverse cerebral systems. Conclusion: The contribution made by the duration of consumption on emotional processing seems far less important than for cognitive processes. Drug abusers seem to be slower on elaboration of emotions and, in particular, of disgust emotion. Considering that the participants were not impaired in cognition, our data support the hypothesis that emotional impairments emerge independently from damage to cognitive functions.

Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require a large computation power and processing time. In this work, we evaluate the speed-accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed-accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113 what is very promising for the forensic field.

Efficiency serves as a crucial metric in assessing the advancement of scientific research for universities. Reasonable inputs of human resources, finances, materials, and other resources, along with high-quality research output, are important guarantees for the research efficiency of Chinese universities. This study delves into the patterns of changes of research efficiency among universities across the 31 provinces and municipalities of China from 2012 to 2022. By scrutinizing input-output perspectives and utilizing data sourced from the China Statistical Yearbook, the analysis unveils prevailing scenarios of input redundancy and output insufficiency in research efficiency across universities. The static and dynamic trends in research efficiency were further investigated by employing a static non-parametric Data Envelopment Analysis (DEA)-BCC model and a dynamic Malmquist index. Key findings from the analysis from 2012 to 2022 include: (1) notable growth in investments in teaching, research equipment assets, and research and development funds among nationwide universities; (2) a steady increase in the mean value of technical efficiency (TE) from 0.97 to 0.99, indicating continuous enhancement in overall efficiency in research input resource allocation across the provinces; (3) the mean Malmquist productivity index of 1.285, with an average annual increase of 28.5%, signifying a consistent improvement in university research efficiency throughout the study period; and (4) the provinces experiencing varying degrees of input redundancy or output insufficiency were selected to suggest the need for tailored strategies in resource allocation. The study underscores the importance of a holistic evaluation system involving stakeholders from government, universities, and society to optimize research efficiency and promote scientific innovation and social development.

The promised dataset was obtained from the daily Turkish words and phrases pronounced by various people in the videos posted on YouTube. The purpose of collecting the dataset is to provide detection of the spoken word by recognizing patterns or classifying lip movements with supervised, unsupervised, semi-supervised learning and machine learning algorithms. Most of the datasets related with lip reading consist of people recorded on camera with fixed backgrounds and the same conditions, but the dataset presented here consists of images compatible with machine learning models developed for real-life challenges. It contains a total of 2335 instances taken from TV series, movies, vlogs, and song clips on YouTube. The images in the dataset vary due to factors such as the way people say words, accent, speaking rate, gender and age. Furthermore, the instances in the dataset consist of videos with different angles, shadows, resolution, and brightness that are not created manually. The most important feature of our lip reading dataset is that we contribute to the non-synthetic Turkish dataset pool, which does not have wide dataset varieties. Machine learning studies can be carried out in many areas, such as the defense industry and social life, with this dataset.

The continuous nature of space and time is a fundamental tenet of many scientific endeavours. That digital representation imposes granularity is well recognized but whether it is possible to address space completely remains unanswered. Part 1 argues that Hales’ proof of Keppler’s conjecture on the packing of hard spheres suggests the answer to be ‘no’, providing examples of why this matters in GIS generally and spatio-temporal GIS in particular. Part 2 seeks to resolve the dichotomy between continuous and granular space, showing how a continuous space may be emergent over a random graph. However, its projection into 3D/4D imposes granularity. Perhaps surprisingly, representing space and time as locally conjugate may be key to addressing a ‘smooth’ spatial continuum. This insight leads to the suggestion of Face Centered Cubic Packing as a space-time topology but also raises further questions for spatio-temporal representation.

Increased demand for wearing face masks, lack of surgical masks and N95 respirators during the coronavirus pandemic created alternative needs for cloth masks. Therefore, to determine filtration efficacy, reusability and duration of wearing new and hand washed cloth masks, the bacteria filtration efficacy was tested using Staphylococcus aureus and Escherichia coli. Cotton cloth masks; single layer without pleats and with pleats, double layer with canvas filter in the middle, double layer with stiff filter in the middle, double layer with pleats on both sides and locally made and imported surgical masks and N95 respirators were used. Bacterial suspension was sprayed over outer surface and swabbed from inner surface at 0hrs, 4thhr, 8thhr and 12thhr. Similar procedure was performed to hand washed and ironed cotton cloth masks. After 24 hours of incubation, single layer without pleats and with pleats had no filtration efficacy(0-100%). Double layer with stiff filter in the middle and double layer with pleats on both sides had higher filtration efficacy(99.96-100%), this filtration efficacy remained up to three occasions of hand washing with soap and water, conferring protection up to 12hours. Filtration efficacy of surgical masks made locally, imported and N95 respirators were 90-100%, 99.99-100%, and 99.99-100% respectively.

The purpose of this study was to develop a mathematical model for non-contacting face seals to analyze how their performance is affected by thermoelastic phenomena. The model was used to solve thermal conductivity and thermoelasticity problems. The primary goal was to calculate the values of thermal deformations of the sealing rings in a non-contacting face seal with a flexibly mounted rotor (FMR) for a turbomachine. The model assumes conversion of mechanical energy into heat in the fluid film. The heat flux generated in the fluid film is transferred first to the sealing rings and then to the fluid surrounding them. An asymmetric distribution of temperature within the sealing rings leads to the occurrence of thermal stresses and, consequently, a change in the rings geometry. The model is solved analytically. The distributions of temperature fields for the sealing rings in the cross-sections are calculated using the Fourier-Bessel series as a superficial function of two variables (r,z). The thermoelasticity problems described by the Navier equations are solved by applying the Boussinesq harmonic functions and Goodier’s thermoelastic displacement potential function. The proposed method involves solving various theoretical and practical problems of thermoelasticity in FMR-type non-contacting face seals. The calculated thermal deformations of the sealing rings are used to determine the most important seal performance parameters such as the leakage rate and power loss.

Face milling is a well known commercial process highly used in heavy industries that consumes high amount of power. Besides power issue, modern manufacturing industries are aiming for per part cost reduction keeping the product quality unimpaired. Unexpectedly if the part is rejected in any stage of manufacturing, the cost of manufacturing dramatically increases. Major cause of part rejection is excessive tool wear that imparts poor surface profile or catastrophic tool failure that causes adherence of broken tool debris onto machined surface. Furthermore, the tool wear is associated with sliding distance (frictional distance) and the tool life quantifies the cost of tools. As such, from the perspective of manufacturing industries it is imperative to optimize the surface quality parameter, cost of part, power consumption, and material removal – this is exactly what is accomplished here. By this work, it is possible to conserve power consumption, produce parts with lower cost, manufacture with uncompromising surface quality and enhanced material removal rate. Moreover, as intermediate factors of interest, the influences of sliding distance, tool life and tool flank wear on the overall machining performance are evaluated. The multi-objective optimization by Grey Relational Analysis (GRA) revealed that for improved product performance and fast manufacturing (case 1) optimum results are: feed per tooth fz = 0.25 mm/tooth, cutting speed vc = 392.6 m/min and cutting length l = 0.5 mm; for resource conservation (case 2) the optimum results are: feed per tooth fz = 0.125 mm/tooth, cutting speed vc = 392.6 m/min, cutting length l = 0.5 mm.

Energy dissipation is one of the most important factors in choosing stepped spillways. However, few studies are about energy dissipation with different horizontal face angles. In this paper, the realizable k-ε turbulence model was used to study the flow field and energy dissipation rates of different stepped spillways with five horizontal face angles in the skimming flow regions. It shows that the free water surfaces and energy dissipation rates change with horizontal face angles and the energy dissipation rates initially decrease and then increase as the horizontal face angle increases. And the horizontal face angles cause the great changes of flow field.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study develops a large-scale benchmark dataset, Sheepface-107, comprised of 5,350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol is developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

During the COVID-19 pandemic face masks grew in importance as their use by the general population was recommended by health officials in order to minimize the risk of infection and prevent further spread of the virus. To ensure health protection of medical personal and other system relevant staff, it is of considerable interest to quickly test if a certain lot of filtering facepiece masks meets the requirements or if the permeability changes under different conditions. As certified penetrometers are rather expensive and were difficult to obtain during the COVID-19 pandemic, we describe two quite simple and cheap methods to quickly test the filter permeability based on an electronic cigarette. The first method uses a precision scale, the second method uses a light scattering detector to measure the filter penetration. To make sure these two methods yield reliable results, both were tested with freshly cut filter samples covering the range of approx. 7% to 60% permeability and compared to the results of a certified penetrometer. The comparison of the two methods with the certified penetrometer showed a good correlation and therefore allow a quick and rather reliable estimation of the permeability. Several examples about the use of faulty masks and the resulting health risks show that simple, fast, cheap and broadly available methods for filter characterization might be useful in these days.

Shape memory alloys (SMAs) are increasingly used in the fields of aviation, automotive and biomedicine due to their unique properties. Nickel-Titanium (NiTi) alloy materials, which are one of the shape memory alloys, are among the most frequently used alloy materials. The shape memory and super elastic effects of NiTi alloys, high ductility and deformation hardening make it difficult to shape burr. An additional problem is the formation of a white layer during machining. In this study, surface milling operations were performed in dry cutting conditions with uncoated cutting tools with different nose radii. The processing parameters were determined based on the experience gained as a result of the preliminary tests. Tungsten carbide cutting tools with different nose radii (0.4mm and 0.8mm) were used for the milling operations. Milling was carried out at three different cutting speeds (20, 35, 50 m/min), feed rates (0.03, 0.07, 0.14 mm/tooth), and a constant axial cutting depth (0.7 mm). As a result of our experimental studies, the best tool life was found to be in 0.8 mm nose radius cutting tools at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.264 mm). The minimum average surface roughness was found after milling with 0.8 mm nose radius cutting tool at 20 m/min cutting speed and 0.03 mm/tooth feed rate (0.346 μm). It has been determined that increasing the cutting tool nose radius reduces both the flank wear over the cutting tool and the average surface roughness.

We present a high-throughput nanoindentation study of in-situ bending effects on incipient plastic deformation behavior of polycrystalline and single-crystalline pure aluminum and pure copper at ultra-nano depths (<200nm). We find that hardness displays a statistically inverse dependence on in-plane stress for indentation depths smaller than 10nm, and the dependence disappears for larger indentation depths. In addition, plastic noise in the nanoindentation force and displacement displays statistically robust noise features, independently of applied stresses. Our experimental results suggest the existence of a regime in FCC crystals where ultra-nano hardness is sensitive to residual applied stresses, but plasticity pop-in noise is insensitive to it.

(1) Background: The lateral arm flap has been the very useful choice in the reconstruction of small- to medium-sized defects like hand, extremity, oral head and neck area reconstructions. Its versatile characteristics and surgical feasibility allow this flap to be widely applied, but the reconstructive potential in facial subunit after tumor ablative procedures has never been reported. In this study, we aimed to utilize the advantages of the flap to treat facial temple subunit defect reconstruction. (2) Methods: Between 2020 and 2023, 12 patients underwent temple reconstruction with lateral arm free flaps after malignant tumor wide excision. There were 7 women and 5 men, and the mean patient age was 60.6 years old. Among the patients with cancer, 6 had squamous cell carcinoma, 5 had basal cell carcinoma and 1 had myxofibrosarcoma. All the flaps were elevated under general anesthesia. Aloprostadil(PGE1, Eglandin®) were administered postoperatively. (3) Results: All the flap types were fasciocutaneous type with varied sizes from 3 × 4 to 5 × 7 cm (Average size of flap 22.7cm2) were utilized. The average pedicle length was 6.1cm. The versatility of the lateral arm flap enabled successful coverage of all cases with no specific complications. Good functional outcome and range of motion in the recipient arm were obtained after surgery. (4) Conclusion: The authors successfully verified the advantages of lateral arm flaps in the treatment of medium sized facial subunt temple defect reconstruction.

Face shields (also referred to as visors), goggles and safety glasses have been worn during the COVID-19 pandemic as one measure to control transmission of the virus. However, their effectiveness in controlling facial exposure to cough droplets is not well established and standard tests for evaluating eye protection for this application are limited. A method was developed to evaluate face shields, goggles and safety glasses as a control measure to protect the wearer against cough droplets. The method uses a semi-quantitative assessment of facial droplet deposition. A cough simulator was developed to generate droplets comparable to those from a human cough. The droplets consisted of a UV fluorescent marker (fluorescein) in water. Fourteen face shields, four pairs of goggles and one pair of safety glasses were evaluated by mounting them on two different sizes of breathing manikin head and challenging them with the simulated cough. The manikin head was positioned in seven orientations relative to the cough simulator to represent various potential occupational exposure scenarios, for example, a nurse standing over a patient. Droplet deposition in the eyes, nose and mouth regions were visualised following three ‘coughs’. Face shields, goggles and safety glasses reduced, but did not eliminate exposure to the wearer from droplets such as those produced by a human cough. The level of protection differed based on the design of the personal protective equipment and the relative orientation of the wearer to the cough. For example, face shields and goggles offered the greatest protection when a cough challenge was face on or from above and the least protection when a cough challenge was from below. Face shields were also evaluated as source control to protect others from the wearer. Results suggested that if a coughing person wears a face shield, it can provide some protection from cough droplets to those standing directly in front of the wearer.

Fabric material was coated with Viruferrin™ and tested for its inactivating properties against the pandemic severe acute respiratory syndrome 2 (SARS-CoV-2) and influenza A viruses. A statistically significant (p<0.0001) decrease in the number of infectious virus particles exposed to Viruferrin-treated fabric when compared with the cotton control for both SARS-CoV-2 and influenza A viruses was observed. For both SARS-CoV-2 and influenza A, Viruferrin-treated fabrics experienced a > 99% virus reduction without saliva after five minutes of contact when compared to the positive control at time point 0. Furthermore, the reusability of the Viruferrin treated fabric was demonstrated by stability for up to 10 washes. The level of anti-viral (SARS-CoV-2) activity remained constant from 5 to 10 washes and demonstrated a significant difference (p<0.0001) from the unwashed untreated material. Applications for this treated fabric are far-reaching, and as a biological face covering offers not only a unique 2-way protection but also is unlikely to cause onward touch transmission.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

With end-to-end encryption for video streaming services becoming more popular, network administrators face new challenges in preserving network performance and user experience. Video ads may cause traffic congestion and poor Quality of Experience. Because of the natural variation in user interests and network situations, traditional algorithms for increasing QoE may face limitations. To solve this problem, we suggest a novel method that uses user facial emotion recognition to deduce QoE and study the effect of ads. We use open-access Face Emotion Recognition (FER) datasets and extract facial emotion information from actual observers to train machine learning models. Participants were requested to watch ad videos and provide feedback, which will be used for comparison, training, testing, and validation of our suggested technique. Our tests show that our approach beats the ITU-T P.1203 standard in terms of accuracy by 37.1%. Our method provides a hopeful answer to the problem of increasing user engagement and experience in video streaming services.

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, result in generated results failing to achieve a high identity similarity with the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllablely transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images and controllable face swapping.

A high performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. It relies on the usage of an Ensemble of Regression Trees (ERTs) machine learning method that aligns 68 facial landmarks. Special focus is given on the acceleration of the frame processing using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without reducing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Due to this, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other applications.

There is little doubt that final victories over pandemics, such as COVID-19, are attributed to herd immunity, either through post-disease convalescence or active immunization of a high percentage of the world's population with vaccines, demonstrating protection from infection and transmission, being available in large quantities and at reasonable prices. However, it is assumable that humans with immune defects or immune suppression, e.g., as a consequence of allograft transplantation, cannot be immunized actively nor produce sufficient immune responses to prevent SARS-CoV-2 infections. These subjects desperately need other strategies, such as sophisticated protection measures and active immunization. Hypertonic salt solutions attack vulnerable core areas of viruses, i.e., salt denatures surface proteins and thus prohibits virus penetration of somatic cells. It has to be ensured that somatic proteins are not affected by denaturation regarding this unspecific virus protection. Impregnating filtering facepieces with hypertonic salt solutions is a straightforward way to inactivate viruses and other potential pathogens. As a result of the contact of salt crystals on the filtering facepiece, these pathogens become denatured and inactivated almost quantitatively. Such a strategy could be easily applied to fight against the COVID-19 pandemic and other ones that may occur in the future. Another possible tool to fight the COVID-19 pandemic is passive immunization with antibodies against SARS-CoV-2, preferably from human origin. Such antibodies can be harvested from patients´ human sera, which have successfully survived their SARS-CoV-2 infection. The disadvantage of a rapid decrease of the immunoglobulin titer after infection ends can be overcome by immortalizing antibody-producing B-cells via fusion with, e.g., mouse myeloma cells. The resulting monoclonal antibodies are then of human origin and available in, at least theoretically, unlimited amounts. Finally, dry blood spots are a valuable tool for surveilling the population´s immunity.

As the COVID-19 pandemic begins to abate and national public health systems are treating the SARS-Cov-2 virus as endemic, many public health measures are no longer mandated, but remain recommended with voluntary participation. One of these is the wearing of fitted face masks, initially mandated to contain, or at least slow, the spread of SARS-CoV-2 which is primarily transmitted via aerosols emitted while breathing, coughing, or sneezing. While the habit of once wearing fitted face masks recedes into memory for much of the population, so does the knowledge of the various types of masks that were once en vogue. To create a record for the future, this paper provides the first comprehensive documentation of the nature and range of fitted facemasks that circulated during the COVID-19 pandemic.

The content of this paper is a very significant contribution to the heritage regional reconstruction and conservation interventions. The results about the iconographic analysis of the Early-Renaissance frescoes of St. Leocadia Church, in Chaves, in the north-east of Portugal, were presented for the first time. This paper aims to study facial similarity analysis applied to identification of the personages represented in the 1511-1513 mural paintings of the apse of St. Leocadia Church, in Chaves Municipality (North of Portugal) and those ones in the oil paintings by the Proto-Renaissance Portuguese painter Nuno Gonçalves. In this work, it was explored the feasibility of face recognition technologies in answering many ambiguities concerning Manueline stylistic identity and iconography. A proposal was made to identify the characters, fundamental to the meaning of the frescoes. It has been experimentally proved on 7 characters of St. Leocadia Church’s paintings, in comparison with the referred old master portraits. It was used an in-house trained deep face recognition model to extract facial features from images of 15th and 16th century portraits to compare their degree of similarity. It was undertaken facial similarity analysis of the faces in the formidable portrait gallery of the 15th century Portuguese society represented in the Panels of St. Vicent, which has been subject of national and international research for 130 years. On the other, in the oil painting of St. Peter and St. Paul and Infanta St. Joana, of the same old master. The iconographic interpretation of the mural paintings of St. Leocadia, based in the deep facial features of the images, is plausible: a catechetical purpose; the ritual practices of royal ancestor worship in royal portrait apses of the churches; the Portuguese maritime expansion and the macro-imperial ideology of D. Manuel I.

The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones` TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions in amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.

Effects of the convection flow, atmospheric diffusivity and humidity on evolution and travel distances of exhaled aerosol clouds by an infected person are considered. The aim of this work is to evaluate the importance of aerosol transmission routes and the effectiveness of the 2-metre separation distance policy. A potential impact of use of face masks on the infection transmission rate, and an opportunity to reduce infection in hospitals, care homes and other public spaces by appropriate monitoring and filtering of air are also considered. The results obtained demonstrate that aerosol particles generated by coughing and sneezing can travel over 30 m. Modelling of the evolution of aerosol clouds generated by coughing and sneezing enables us to evaluate the deposition dose of aerosol particles in healthy individuals. For example, a person in a public place (e.g. supermarket or car park) can accumulate in the respiratory system up to 200 virus copies in 2 min time by breathing in virus laden aerosols. Wearing face mask considerably reduces the deposited load down to 2 virus copies per 2 min. The modelling also suggests that it should be possible to measure virus causing COVID-19 (SARS-CoV-2) within aerosol particles in hospitals and public places, e.g. care homes and supermarkets.

In the framework of fully vertical GaN-on-Silicon device technology development, we report on the optimization of non-alloyed ohmic contacts on the N-polar n+ doped GaN face backside layer. This evaluation is made possible by using patterned TLMs (Transmission Line Model) through direct laser writing lithography after locally removing the substrate and buffer layers in order to access the n+ doped backside layer. As deposited non-alloyed metal stack on top of N-polar orientation GaN layer after buffer layers removal results in poor ohmic contact quality. To significantly reduce the related contact resistances, an HCl treatment is applied prior to metallization under various time and temperature conditions. A 3-minutes HCl treatment at 70°C is found to be the optimum condition to achieve thermally stable high ohmic contact quality. To further understand the impact of the wet treatment, SEM (Scanning Electron Microscopy) and XPS (X-ray Photoelectron Spectroscopy) analysis were performed. XPS revealed a decrease in Ga-O concentration after applying the treatment, reflecting the higher oxidation susceptibility of the N-polar face compared to the Ga-polar face, which was used as a reference. SEM images of the treated samples show the formation of pyramids on the N-face after HCl treatment, suggesting specific wet etching planes of the GaN crystal from the N-face. The size of the pyramids is time-dependent; thus, increasing the treatment duration results in larger pyramids, explaining the degradation of ohmic contact quality after prolonged high temperature HCl treatment.

In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.

The consequences of manipulations in structure and amino acid composition of native cyclolinopeptide A (CLA) from linen seeds and its linear precursor on their biological activities and mechanisms of action are reviewed. The modifications included truncation of the peptide chain, replacement of amino acid residues with proteinogenic or non-proteinogenic ones, modifications of peptide bond, and others. The studies revealed changes in the immunosuppressive potency of these analogs investigated in a number of in vitro and in vivo experimental models, predominantly in rodents, as well as differences in their postulated mechanism of action. The modified peptides were compared with cyclosporine A and parent CLA. Some of the synthesized and investigated peptides show potential therapeutic usefulness.

Forensic speaker recognition plays a key role in criminal investigations, providing important conclusions for the justice system. The mandatory use of protection masks during the COVID-19 pandemic has posed a challenge for forensic speaker recognition, as they act as voice barriers or filters. Although the pandemic has been declared over, analyzing their impact in forensic speaker recognition contributes to a better understanding of the use of coverings as a voice disguise technique. This study aims to evaluate the impact of two types of face masks on an automatic forensic speech recognition system. For this purpose, the Multilingual Forensic Voice Database (FMVD), developed under the CERTAIN-FORS project, funded by the European Union, was used. Comparisons were made between dialog speech samples and reading samples collected without a mask, with a surgical mask, and with an FFP2 mask for both sexes in eight different languages. The performance metrics equal error rate (EER) and cost of likelihood ratio (Cllr) were calculated and analyzed. The results show that the presence of face masks has an impact on the performance metrics. The effect observed varies according to the language spoken, the gender of the speaker, and the type of mask.

Lotions with or without 28% ascorbyl glucoside arginine complex (AGAC) were applied twice daily for 24 weeks in a double‐blind half‐face study of 27 Japanese females with solar lentigos (SLs). Pigmentation scores were evaluated using a photo‐scale and skin colors were assessed using a color difference meter and a Mexameter. Treatment with the test lotion elicited a significant decrease in pigment scores at 24 weeks compared to week 0; with a significant decrease in pigment scores at 24 weeks compared to the placebo lotion‐treated SLs. In the test lotion‐treated SLs; the L values and melanin index (MI) significantly (p<0.0001) increased and decreased; respectively; at 12 and 24 weeks of treatment compared to week 0. Comparisons of increased L (△L) values or decreased MI (△MI) values between test and placebo lotion‐treated SLs demonstrated that the test lotion‐treated SLs had significantly higher △L or △MI values than the placebo lotion‐treated SLs both at 12 and 24 weeks of treatment. The sum of our results strongly indicates that AGAC is distinctly effective in ameliorating the hyperpigmentation levels of SLs at a visibly recognizable level by the subjects without any hypo‐pigmenting effects or skin irritation.

Equipped with an early social predisposition immediately post-birth, humans typically form associations with mothers and other family members through exposure learning, canalized by a spontaneous predisposition to biological motion, face configuration, and other cues of animacy. If impaired, reduced social preferences can lead to social interaction impairments such as autism spectrum disorder (ASD) via misguided canalization. Despite being taxonomically distant, domestic chicks also follow a homologous developmental trajectory toward adaptive socialization through imprinting, which is guided via predisposed preferences that are homologous to those of humans, thereby suggesting that chicks are valid animal models of ASD. In addition to the convergent similarities in predisposition with human newborns, accumulating evidence suggests the construct validity of the chick model. Considering the recent progress in the evolutionary neurobiology of vertebrates, we reviewed the advantages and limitations of the chick model of developmental mental diseases in humans.

Local binary pattern is one of the visual descriptors and can be used as a powerful feature extractor for texture classification. In this paper, a novel representation for face recognition is proposed, called it Bilateral Line Local Binary Patterns (BL-LBP). This scheme is an extension of Line Local Binary Patterns descriptors in the statistical learning subspace. The present bilateral descriptors are fused with an ensemble learning of calibrated SVM models. The performance of this scheme is evaluated using 5 standard face databases. It is found that it is robust against illumination variation, diverse facial expressions and head pose variations and its recognition accuracy reaches 98 percent, running on a mobile device with a processing speed of 63 ms per face. Results suggest that our proposed method can be very useful for the vision systems that have limited resources where the computational cost is critical.

In today’s world face detection is the most important task. Due to the chromosomes disorder sometimes a human face suffers from different abnormalities. For example, one eye is bigger than the other, cliff face, different chin-length, variation of nose length, length or width of lips are different, etc. For computer vision currently this is a challenging task to detect normal and abnormal face and facial parts from an input image. In this research paper a method is proposed that can detect normal or abnormal faces from a frontal input image. This method used Fast Fourier Transformation (FFT) and Discrete Cosine Transformation of frequency domain and spatial domain analysis to detect those faces.

A face serum composed of a combination of biologically active compounds was evaluated for safety and efficacy in vitro, in a repeat insult patch test and in a human clinical efficacy trial. The serum inhibited tyrosinase activity modestly, decreased collagenase activity and exhibited notable free radical scavenging activity in vitro. It is gentle to the skin, as the serum did not irritate the skin or produce symptoms of allergic contact dermatitis in the 55 healthy adults that participated in the repeat insult patch test. In the efficacy trial, daily application of the face serum for 30 days significantly increased skin hydration, with all 35 volunteers experiencing improvement. Substantial improvements in skin elasticity, roughness (fine lines and wrinkles), and brightness also occurred during the trial. Dermatological examination also revealed a trend for reduced comedone count with use of the serum. Self-assessment responses revealed that all volunteers experienced improvements in multiple skin quality parameters and that participant perceptions are consistent with the results of the instrumental analyses. These findings indicated that the measured improvements in skin quality are not only statistically significant but are also clinically relevant as they were great enough for users of the face serum to feel and recognize.

In today's world it is very much important to maintain the security of information and its risks. The biometric-based techniques are very much useful in these problems. Among the several kinds of biometric-based technique, face detection is much complex and much more important. Due to the age and several other problems, a human face structure changes over time, again a human has lots of expressions. Sometimes due to the lighting condition or the variation of the angle of an input device, the pattern of a human face structure also changed. As a result, the face cannot be detected properly. In this paper, a method is proposed that can detect the human faces both automatically and manually very efficiently. In manual mode, a user can select the input faces referred by the system according to their choice. In automated mode, the system detected all possible face areas using the Kohonen Self-Organizing Feature Map technique. This method reduced the complex color image into a vector quantized image with desired colors. Then a color segmentation technique is used to detect the possible face skin areas from the vector quantized image. Then the Histogram Oriented Gradient technique used to detect the feature from the faces and K-Nearest Neighbour Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method.

Many individuals are wearing face masks improperly at ‘half mask’ and potentially breathing out microbes that can contaminate the air as well as anything below the nose. This note provides the first report that bacteria and fungi breathed out during nasal air exhalation are able to be cultured after landing on blood agar plates. The CFU’s are higher after both 10 breaths and extremely significant for 20 breaths compared to the control plates exposed to the air. Implications of this finding are that going ‘nose commando’ may be able to continue the spread of respiratory diseases such as COVID-19.

The disruption of conventional manufacturing, supply, and distribution channels for medical supplies during the COVID-19 pandemic has caused widespread shortages and catalyzed local efforts to use nontraditional, rapid manufacturing to meet urgent healthcare needs. Here we present a crisis-responsive design framework designed to assist with product development under pandemic conditions. The framework utilizes extensive stakeholder engagement, comprehensive and dynamic needs assessment, local manufacturing, and product testing for the accelerated development of healthcare products. We contrast this framework with traditional medical device manufacturing and discuss relevant regulatory policies. We highlight the applicability of the crisis-responsive framework to a successful local program that designed and supplied face shields for a large US academic hospital. Finally, we make recommendations aimed at improving future resilience to healthcare emergencies. These include continued development of open source designs suitable for rapid manufacturing and changes in regulatory policy that strike a balance between rigidity and uncontrolled innovation.

Based on the nonlinear failure criterion and modified tangential technique, the upper bound solutions of supporting pressure on the deep tunnel face were obtained under pore water pressure conditions. The influence of parameters on the supporting pressure and collapse range was investigated according to the unlimited block failure mechanism. It is found that the upper bound solutions of supporting pressure increase with the growth of the nonlinear coefficient and pore water pressure coefficient. The collapse range of the tunnel face scales out with the increase of the nonlinear coefficient and shrinks when increasing the pore water pressure coefficient. Moreover, with the increase of the nonlinear coefficient, the impact strength on supporting pressure and collapse range declines gradually. According to the calculating results, it is manifest that both the pore water pressure and nonlinear criterion factors have negative impacts on the stability of the tunnel face. Thus, more attention should be paid to these parameters to ensure face stability in deep tunnel construction.

Although mask-wearing is now widespread, knowledge of how to quantify or improve its performance remains surprisingly limited and largely based on empirical evidence. The objective of this study was to visualize the expiratory airflows from facemasks and evaluate aerosol transmission between two persons. Different visualization methods were explored, including the Schlieren optical system, laser/LED-particle imaging system, thermal camera, and vapor-SarGel system. The leakage flows and escaped aerosols were quantified using a hotwire anemometer and a particle counter, respectively. The results show that mask-wearing reduces the exhaled flow velocity from 2~4 m/s (with no facemask) to around 0.1 m/s, thus decreasing droplet transmission speeds. Cloth, surgical, and KN95 masks showed varying leakage flows at the nose top, sides, and chin. The leakage rate also differed between inhalation and exhalation. The neck gaiter has low filtration efficiency and high leakage fractions, providing low protection efficiency. There was considerable deposition in the mouth-nose area, as well as the neck, chin, and jaw, which heightened the risk of self-inoculation through spontaneous face-touching. A face shield plus surgical mask greatly reduced droplets on the head, neck, and face, indicating that double face coverings can be highly effective when a single mask is insufficient. The vapor-SarGel system provided a practical approach to study interpersonal transmission under varying close contact scenarios or with different face coverings.

This paper presents the development and implementation of a Pan-Tilt-Zoom (PTZ) camera interfaced with a Jetson processor hosting Artificial Intelligence (AI) algorithms, designed to address the challenges of traditional biometric capture for first-time passport photos of young children. The system enhances the accuracy, efficiency, and adaptability of facial biometric capture by recognizing and accommodating the dynamic and diverse behaviors of children. Utilizing AI, the system detects faces and analyzes facial features using 68 landmark points, ensuring high-quality images that meet ISO standards. These technical specifications, implementation process, and performance evaluations of the developed system are provided in this paper, demonstrating the potential of the proposed AI Face-Capture to advance biometric identification and verification for young children.

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

Polymers in highly confined geometries can display complex morphologies including ordered phases. A basic component of a theoretical analysis of their phase behavior in confined geometries is the knowledge of the number of possible single-chain conformations compatible with the geometrical restrictions and the established crystalline morphology. While the statistical properties of unrestricted self-avoiding random walks (SAWs) both on and off-lattice are very well known, the same is not true for SAWs in confined geometries. The purpose of this contribution is a) to enumerate the number of SAWs on the simple cubic (SC) and face-centered cubic (FCC) lattices under confinement for moderate SAW lengths, and b) to obtain an approximate expression for their behavior as a function of chain length, type of lattice, and degree of confinement. This information is an essential requirement for the understanding and prediction of entropy-driven phase transitions of model polymer chains under confinement. In addition, a simple geometric argument is presented that explains, to first order, the dependence of the number of restricted SAWs on the type of SAW origin.

Transparent materials used for facial protection equipment provide protection against microbial infections caused by viruses and bacteria, including multidrug-resistant strains. However, transparent materials used for this type of application are made of materials that do not possess antimicrobial activity. They just avoid direct contact between the person and the biological agent. Therefore, healthy people can get infected through contact of the contaminated material surfaces and this equipment constitute an increasing source of infectious biological waste. Furthermore, infected people can transmit microbial infections easily because the protective equipment do not inactivate the microbial load generated while breathing, sneezing, or coughing. In this regard, the goal of this work consisted of fabricating a transparent face shield with intrinsic antimicrobial activity that could provide extra-protection against infectious agents and reduce the generation of infectious waste. Thus, a single-use transparent antimicrobial face shield composed of polyethylene terephthalate and an antimicrobial coating of benzalkonium chloride has been developed for the next generation of facial protective equipment. The antimicrobial coating was analyzed by atomic force microscopy and field emission scanning electron microscopy with elemental analysis. This is the first facial transparent protective material capable of inactivating enveloped viruses such as SARS-CoV-2 in less than one minute of contact, and the methicillin-resistant Staphylococcus aureus and Staphylococcus epidermidis. Bacterial infections contribute to severe pneumonia associated with the SARS-CoV-2 infection, and their resistance to antibiotics is increasing. Our extra protective broad-spectrum antimicrobial composite material could also be applied for the fabrication of other facial protective tools such as such as goggles, helmets, plastic masks and space separation screens used for counters or vehicles. This low-cost technology would be very useful to combat the current COVID-19 pandemic and protect health care workers from multidrug-resistant infections in developed and underdeveloped countries.

Recent simulation studies have revealed a wealth of distinct crystal polymorphs encountered in the self-organization of polymer systems driven by entropy or free energy. The present analysis, based on the concept of self-avoiding random walks on crystal lattices, is useful to calculate upper bounds for the entropy difference of the crystals that are formed during polymer crystallization and thus provide predictions on polymorph thermodynamic stability. Here, we compare two pairs of crystals sharing the same coordination number, ncoord: hexagonal close packed (HCP) and face centered cubic (FCC), both having ncoord = 12 and the same packing density, and the less dense hexagonal (HEX) and body centered cubic (BCC) lattices, with ncoord = 8. In both cases, once a critical step length is reached, one of the crystals shows a higher number of SAWs compatible with the crystal. We explain the observed trends in terms of the bending and torsion angles corresponding to the different chain geometry as imposed by the geometric constraints of the crystal lattice.

Thin Pd-40%Cu films were obtained by the classical melting and rolling method, magnetron sputtering and modified with nanostructured functional coatings to intensify the process of hydrogen transport. The films were modified by electrodeposition according to the classical method of obtaining palladium black and "Pd-Au nanoflowers" with spherical and pentagonal particles, respectively. The experiment results demonstrated the highest catalytic activity (89.47 mA cm–2), good resistance to CO poisoning and long-term stability of Pd-40%Cu films with a pentagonal structured coating. The investigation of the developed membranes in the hydrogen transport processes in the temperature range of 25-300°C also demonstrated high and stable fluxes up to 475.28 mmol s–1 m–2 (deposited membranes) and 59.41 mmol s–1 m–2 (dense-metal membranes), which turned out to be up to 1.5 higher compared to membrane materials with classic niello. For all-metal modified membranes, the increase in flux was up to 7 times, compared with a smooth membrane made of pure palladium, and for deposited films, this difference was several tens of times. The membrane materials selectivity was also high up to 4419. The developed strategy for modifying membrane materials with functional coatings of a fundamentally new complex geometry can shed new light on the development and fabrication of durable and highly selective palladium-based membranes for gas steam reformers.

The self-assembly is an intriguing process of great importance in general soft matter and thus re-ceives an ever-growing scientific interest. Its most trivial example is the entropy-driven crystalli-zation of hard spheres. Past works have established the similarities and differences in the phase behavior of monomers and chains made of hard spheres. In the present work, inspired by the dif-ference in the melting points of the pure components, we study, through Monte Carlo simulations, the phase behavior of athermal mixtures composed of fully flexible polymers and individual monomers of uniform size. We analyze how the relative number fraction and the packing density affect crystallization and the established ordered morphologies. A synergetic effect is observed in the crystallization leading to synchronous crystallization of the two species. Structural analysis of the resulting ordered morphologies shows perfect mixing and thus no phase separation, which could split the system into polymer-rich and monomer-rich domains. Due to the constraints imposed by chain connectivity, the local environment of the individual spheres is systematically more spherical and more symmetric compared to the one of spheres belonging to chains. In general, increasing polymer content leads to reduction of the degree of crystallinity. According to the present findings, relative concentration is another determining factor to control the phase behavior of hard colloidal systems based on polymers.

Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

Reciprocal Personal/Public Protection (RPPP) featured with source control is introduced, Mask Discriminating Mouth and Nose (MDMN) is employed to serve the purpose, which includes polymer based mouth cover with optional nose cover. The new knowledge that mouth is a primary, active and dominant source of the virus has been well established, which is the base of MDMN. Source classification and related source control tools are discussed, mouth cover is recommended as the tool prioritized to use. Liquid droplets is identified as a hard issue related to mask, liquid droplets, mask fitting, comfort and facial recognition constitute real challenges of mask in addition to efficiency, All of these have been addressed with MDMN. Comparisons between MDMN and masks/face covering are taken on four aspects: efficiency and efficacy, tolerance and comfort, cost and waste, and civil rights and public interest. Mouth cover is recommended to replace the face covering and act as both a personal tool and a public utensil, mouth cover with nose cover can provide better protection than N95 etc. RPPP with MDMN, could be an alternative for lockdown, a parallel strategy to vaccine, and a collectively living way during the pandemic era. MDMN, featured with high efficiency protection, high degree comfort, easy wearing, tight-fitting, easy facial recognition & communication, reusability, cost-effective, environment friendly and scale manufacturing more readily and widely etc., is a simple and sustainable solution, which is essential for ordinary people to keep wearing it properly for protection.

Alzheimer’s Disease (AD) has no cure. Earlier, we showed that partial inhibition of mitochondrial complex I (MCI) with small molecule CP2 induces adaptive stress response activating multiple neuroprotective mechanisms. Chronic treatment reduced inflammation, improved synaptic and mitochondrial functions, and blocked neurodegeneration in symptomatic APP/PS1 mice, a translational model of AD. Here, using serial block-face scanning electron microscopy (SBFSEM) and three-dimensional (3D) EM reconstructions combined with Western blot analysis and next-generation RNA sequencing, we demonstrate that CP2 treatment also restores mitochondrial morphology and mitochondria-endoplasmic reticulum (ER) communication in the APP/PS1 mouse brain. Using 3D EM volume reconstructions, we show that mitochondria in AD dendrites exist primarily as mitochondria-on-a-string (MOAS). Compared to other morphological phenotypes, MOAS are extensively enveloped in the ER membranes forming multiple mitochondria-ER contact sites (MERCS) known to contribute to abnormal lipid and calcium homeostasis. CP2 treatment specifically reduced MOAS formation, consistent with improved energy homeostasis in the brain, with concomitant reduction in MERCS, ER stress, and improved lipid homeostasis. These data provide novel information on the role MOAS play in AD and additional support for further development of partial MCI inhibitors as disease modifying strategy for AD.

We present results on the spontaneous crystallization of freely-jointed polymers of hard spheres obtained in an unprecedentedly long Monte Carlo (MC) simulation on a system of 54 chains of 1000 monomers. Starting from a purely amorphous configuration and after a transitory dominance of the hexagonal closed packed (HCP) polymorph, the system crystallizes in a final, stable, face centered cubic (FCC) crystal of very high perfection. Through refined metrics we gauge the degree of ordering and identify the regions of the phase transition and the corresponding morphologies. An analysis of chain conformational characteristics, of the spatial distribution of monomers and of the volume accessible to them shows that the phase transition is caused by an increase in translational entropy that is larger than the loss of conformational entropy of the chains in the crystal compared to the amorphous state. Polymer chains in the crystal adopt ideal random walk statistics as their great length renders local conformational details, imposed by the geometry of the FCC crystal, irrelevant.

The use of Artificial Neural Networks (ANN) is a great contribution to medical studies since the application of forecasting concepts allows the analysis of future diseases propagations. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were proposed two methodologies to predict 28 days ahead the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, United Kingdom, and Germany, and a case study of the results of massive immunization in Israel. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries size, and the cumulative vaccination values by the percentage of population immunized, at least with one dose of vaccine. As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology and targets other possibilities of use for the proposed method. The best architecture achieved a general MAE for the 1 to 28 days ahead forecast lower than 30 cases, 0,6 deaths and 2,5 ICU patients by million people.

The local and global order in dense packings of linear, semi-flexible polymers of tangent hard spheres are studied by employing extensive Monte Carlo simulations at increasing volume fractions. Chain stiffness is controlled by a tunable harmonic potential for the bending angle whose intensity dictates the rigidity of the polymer backbone as a function of the bending constant and equilibrium angle. The studied angles range from acute to obtuse ones, reaching the limit of rod-like polymers. We analyze how packing density and chain stiffness affect the ability of chains to self-organize at the local and global levels. The former corresponds to crystallinity as quantified by the Characteristic Crystallographic Element (CCE) norm descriptor, while the latter is computed through the scalar orientational order parameter. In all cases, we identify the critical volume fraction for the phase transition and gauge the established crystal morphologies, developing a complete phase diagram as a function of packing density and equilibrium bending angle. A plethora of structures is obtained, ranging from random hexagonal closed packed morphologies of mixed character and almost perfect face centered cubic (FCC) and hexagonal close-packed (HCP) crystals at the level of monomers, to nematic mesophases, with prolate and oblate mesogens at the level of chains. For rod-like chains, hysteresis is observed between the establishment of long-range nematic order and crystallization, while for right-angle chains both transitions are synchronized. A comparison is also provided against the analogous packings of monomeric and fully flexible chains of hard spheres.

The article suggests methods that allow creating the most complicated type of polygonal masonry found in Peru. This masonry type consists of large stone blocks weighing from several hundred kilograms to several tons fitted close to each other almost without a gap between complicated curved surfaces over a large area. The work provides a description of techniques, which apparently were used by builders who arrived from Europe. The techniques under discussion are based on the use of a reduced clay model, 3D-pantograph, topography translator and replicas. The use of the topography translator, reduced clay model and pantograph provides not only the unique appearance and high quality of masonry of large blocks, but also allows to increase the productivity of the builders significantly. As machines coping-scaling three-dimensional objects are known since the beginning of the 18th century, the stone structures under consideration should be approximately dated by this time. The remaining simpler types of polygonal masonry, when the stones are small or the fitted surfaces are almost flat, or the stones contact each other over a small area, or there are significant gaps between the stones, are quite consistent with the well-known methods of stone processing at that time or earlier, and, therefore, they do not require any additional explanations. The Fortress Sacsayhuaman is considered as an example of early star fortresses that has survived to our time. The polygonal structures in Peru, the polygonal Face Towers and polygonal bas-reliefs in Cambodia, symmetrical statues of pharaohs in Egypt are based on the same construction technologies, working methods, tools and technical contrivances. Therefore, with a high probability one can state that all these monuments were created by the same group of architects, sculptors, builders, and could not have appeared before the 17th century.

In the present work we revise and extend the Characteristic Crystallographic Element (CCE) norm, an algorithm used to simultaneously detect radial and orientational similarity of computer-generated structures with respect to specific reference crystals and local symmetries. Based on the identification of point group symmetry elements, the CCE descriptor is able to gauge local structure with high precision and finely distinguish between competing morphologies. As test cases we use computer-generated monomeric and polymer systems of spherical particles interacting with the hard-sphere and square-well attractive potentials. We demonstrate that the CCE norm is able to detect and differentiate, between others, among: hexagonal close packed (HCP), face centered cubic (FCC), hexagonal (HEX) and body centered cubic (BCC) crystals as well as non-crystallographic fivefold (FIV) local symmetry in bulk 3-D systems; triangular (TRI), square (SQU) and honeycomb (HON) crystals, as well as pentagonal (PEN) local symmetry in thin films of one-layer thickness (2-D systems). The descriptor is general and can be applied to identify the symmetry elements of any point group for arbitrary atomic or particulate system in two or three dimensions, in the bulk or under confinement.